Set-up project naming from reproducible code

Set-up

1 Import clean OTU tables

All EDGE samples excluded
OTU tables rarefied to the following sequencing depths:

16S: 9,048
ITS: 10,116

Bacterial & Archaeal Dataset:

23,156 OTUs
533 samples

Fungal Dataset:

6,749 OTUs
509 samples

Set-up metadata strata

Available metadata

Available metadata:

##  [1] "SampleID"       "ITS_ID"         "sample_name"   
##  [4] "Replicate"      "Site"           "Site_Grass"    
##  [7] "Site.Rep"       "Grass"          "Grass_Rep"     
## [10] "Edge"           "SiteName"       "MAT_degC"      
## [13] "MAP_mm"         "CollectionDate" "Latitude"      
## [16] "Longitude"      "Elevation_m"    "Grassland"     
## [19] "Gradient"       "Bin"            "julianday"     
## [22] "coll_GDD"       "GDD30yr"        "GDD3yr"        
## [25] "GDD2015"        "ppt30yr"        "ppt3yr"        
## [28] "ppt2015"        "soil_moisture"  "GWC"           
## [31] "perc_moisture"  "pH"             "phos"          
## [34] "ammonium"       "nitrate"        "SOM"           
## [37] "herbivory_perc" "avg_SLA"        "avg_SRL"

Set-up taxonomy for plotting

1.1 Global: Calculate mean relative abundance by tax rank and export csv of relative abundance

1.1.1 merge samples within dataset

1.1.2 tax glom and export

(no generating code just copy and replace)

Samples: Calculate relative abundance by tax rank and export csv of relative abundance

create vectors of top 10 taxa names to plot for all ranks

auto-generated code found:

set minimum threshold of 0.5% all-sample (a.k.a global) mean relative abundance - (so that taxa below this threshold won’t get plotted)

tax glom by each rank
transform to relative abundance
psmelt
add column “[rank]_all” to preserve names not in top 10
recode all taxa not in top 10 to by “LowAbundance” & revalue with specifics later (in dat_Bac.[rank].r and in dat_Fun.[rank].r)

1.1.3 generating code:

Bac.rank.names<-rank_names(Bac)[2:6]
# add this line to beginning
  #  cat('\n # Set min plotting threshold to 0.5% relative abundance (all-sample a.k.a global mean) \n')

#also adding psmelt step

for(rank in Bac.rank.names) {

  cat('\n## \n')

  cat('################## \n')

  cat('# Bac:', rank, '# \n')

  cat('################## \n')

  cat('\n')
  
   cat('min_nname_Bac_', rank, '<-length(unique(Bac_', rank, '_Mean_Abund$', 
       rank, '[Bac_', rank, '_Mean_Abund$Bac_', rank, '_Mean > 0.5]))',
       sep = '')
  
  cat('\n \n ')

    cat('if (min_nname_Bac_', rank, ' < 10){
  Bac.', rank, '.names.to.plot<-top_n(Bac_', rank, '_Mean_Abund, min_nname_Bac_', rank, ', Bac_', rank, '_Mean)$',rank, ' 
} else {
  Bac.', rank, '.names.to.plot<-top_n(Bac_', rank, '_Mean_Abund, 10, Bac_', rank, '_Mean)$', rank, '
}', sep = '')

  cat('\n# \n')
  
  cat('\nBac.',rank,'.A <- tax_glom(Bac, taxrank="',rank,'")\n',sep = '')
  
  cat('Bac.',rank,' <- prune_taxa(taxa_sums(Bac.',rank,'.A) > 0, Bac.',rank,'.A)\n',sep = '')

  cat('Bac.',rank,'.r  = transform_sample_counts(Bac.',rank,', function(x) x / sum(x) )\n',sep = '')

  cat('dat_Bac.',rank,'.r <- psmelt(Bac.',rank,'.r)\n',sep = '')

  cat('dat_Bac.',rank,'.r$',rank,' <- as.character(dat_Bac.',rank,'.r$',rank,') \n',sep = '')

  cat('dat_Bac.',rank,'.r$',rank,'_all <- dat_Bac.',rank,'.r$',rank,' \n',sep = '')
  
  cat('dat_Bac.',rank,'.r$',rank,' <- if_else(dat_Bac.',rank,'.r$',rank,' %in% Bac.',rank,'.names.to.plot, dat_Bac.',rank,'.r$',rank,', "LowAbundance") \n',sep = '')

  cat('\n## \n')

  cat('\n \n')

}

Fun.rank.names<-rank_names(Fun)[2:7] #change 6 to 7 if fungal

# add this line to beginning
  #  cat('\n # Set min plotting threshold to 0.5% relative abundance (all-sample a.k.a global mean) \n')

#also adding psmelt step

for(rank in Fun.rank.names) {

  cat('\n## \n')

  cat('################## \n')

  cat('# Fun:', rank, '# \n')

  cat('################## \n')

  cat('\n')
  
   cat('min_nname_Fun_', rank, '<-length(unique(Fun_', rank, '_Mean_Abund$', 
       rank, '[Fun_', rank, '_Mean_Abund$Fun_', rank, '_Mean > 0.5]))',
       sep = '')
  
  cat('\n \n ')

    cat('if (min_nname_Fun_', rank, ' < 10){
  Fun.', rank, '.names.to.plot<-top_n(Fun_', rank, '_Mean_Abund, min_nname_Fun_', rank, ', Fun_', rank, '_Mean)$',rank, ' 
} else {
  Fun.', rank, '.names.to.plot<-top_n(Fun_', rank, '_Mean_Abund, 10, Fun_', rank, '_Mean)$', rank, '
}', sep = '')

  cat('\n# \n')
  
  cat('\nFun.',rank,'.A <- tax_glom(Fun, taxrank="',rank,'")\n',sep = '')
  
  cat('Fun.',rank,' <- prune_taxa(taxa_sums(Fun.',rank,'.A) > 0, Fun.',rank,'.A)\n',sep = '')

  cat('Fun.',rank,'.r  = transform_sample_counts(Fun.',rank,', function(x) x / sum(x) )\n',sep = '')

  cat('dat_Fun.',rank,'.r <- psmelt(Fun.',rank,'.r)\n',sep = '')

  cat('dat_Fun.',rank,'.r$',rank,' <- as.character(dat_Fun.',rank,'.r$',rank,') \n',sep = '')

  cat('dat_Fun.',rank,'.r$',rank,'_all <- dat_Fun.',rank,'.r$',rank,' \n',sep = '')
  
  cat('dat_Fun.',rank,'.r$',rank,' <- if_else(dat_Fun.',rank,'.r$',rank,' %in% Fun.',rank,'.names.to.plot, dat_Fun.',rank,'.r$',rank,', "LowAbundance") \n',sep = '')

  cat('\n## \n')

  cat('\n \n')

}

1.1.4 create vectors of top 10 taxa names

1.1.4.1 Bac

(paste in previous generated code)

1.1.4.2 Fun

1.2 Group low abundance (!%inc% top 10 taxa)

(no generating code just copy paste)

1.2.1 Bac

1.2.2 Fun

#select metadata of interest (do later)

dat_Bac.Phylum.r2<-subset(dat_Bac.Phylum.r, select=c(OTU, Sample, Abundance, Basin, Site, BasinSite, Depth, Depth2, Pit, Phylum))

# save dat files
saveRDS(dat_Bac.Phylum.r2, file = "../processed_data/clean_rds_saves/tax_dat/dat_Bac.Phylum.r2.rds")

1.3 1.4 restore dat files

Full Dataset

Grass replicates, grouped by Site

Phylum

Bacteria-Archaea

Fungi

Class

Bacteria-Archaea

Fungi

Order

Bacteria-Archaea

Fungi

Family

Bacteria-Archaea

Fungi

Site replicates, grouped by Grass

Phylum

Bacteria-Archaea

Fungi

Class

Bacteria-Archaea

Fungi

Order

Bacteria-Archaea

Fungi

Family

Bacteria-Archaea

Fungi

Across Different Sites by Grass Hosts

Phylum

Bacteria-Archaea

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER

BOGR

GMT

BOER

BOGR

GNF

SCSC

HAR

ANGE

BOGR

BUDA

SCSC

HPG

BOGR

BUDA

KAE

ANGE

BUDA

SCSC

KNZ

ANGE

BOGR

BUDA

SCSC

LAR

ANGE

BOGR

SCSC

LBJ

ANGE

BUDA

SCSC

NWP

ANGE

SCSC

ONF

ANGE

SCSC

RNF

BOGR

SEV

BOER

BOGR

SFA

SCSC

UHC

ANGE

SCSC

Fungi

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER

BOGR

GMT

BOER

BOGR

GNF

BOGR

SCSC

HAR

ANGE

BOGR

BUDA

SCSC

HPG

BOGR

BUDA

KAE

ANGE

BUDA

SCSC

KNZ

ANGE

BOGR

BUDA

SCSC

LAR

ANGE

BOGR

SCSC

LBJ

ANGE

BUDA

SCSC

NWP

ANGE

SCSC

ONF

ANGE

SCSC

RNF

BOGR

SCP

ANGE

BOGR

SCSC

SEV

BOER

BOGR

SFA

SCSC

UHC

ANGE

SCSC

Class

Bacteria-Archaea

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER

BOGR

GMT

BOER

BOGR

GNF

SCSC

HAR

ANGE

BOGR

BUDA

SCSC

HPG

BOGR

BUDA

KAE

ANGE

BUDA

SCSC

KNZ

ANGE

BOGR

BUDA

SCSC

LAR

ANGE

BOGR

SCSC

LBJ

ANGE

BUDA

SCSC

NWP

ANGE

SCSC

ONF

ANGE

SCSC

RNF

BOGR

SEV

BOER

BOGR

SFA

SCSC

UHC

ANGE

SCSC

Fungi

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER

BOGR

GMT

BOER

BOGR

GNF

BOGR

SCSC

HAR

ANGE

BOGR

BUDA

SCSC

HPG

BOGR

BUDA

KAE

ANGE

BUDA

SCSC

KNZ

ANGE

BOGR

BUDA

SCSC

LAR

ANGE

BOGR

SCSC

LBJ

ANGE

BUDA

SCSC

NWP

ANGE

SCSC

ONF

ANGE

SCSC

RNF

BOGR

SCP

ANGE

BOGR

SCSC

SEV

BOER

BOGR

SFA

SCSC

UHC

ANGE

SCSC

Order

Bacteria-Archaea

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER

BOGR

GMT

BOER

BOGR

GNF

SCSC

HAR

ANGE

BOGR

BUDA

SCSC

HPG

BOGR

BUDA

KAE

ANGE

BUDA

SCSC

KNZ

ANGE

BOGR

BUDA

SCSC

LAR

ANGE

BOGR

SCSC

LBJ

ANGE

BUDA

SCSC

NWP

ANGE

SCSC

ONF

ANGE

SCSC

RNF

BOGR

SEV

BOER

BOGR

SFA

SCSC

UHC

ANGE

SCSC

Fungi

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER

BOGR

GMT

BOER

BOGR

GNF

BOGR

SCSC

HAR

ANGE

BOGR

BUDA

SCSC

HPG

BOGR

BUDA

KAE

ANGE

BUDA

SCSC

KNZ

ANGE

BOGR

BUDA

SCSC

LAR

ANGE

BOGR

SCSC

LBJ

ANGE

BUDA

SCSC

NWP

ANGE

SCSC

ONF

ANGE

SCSC

RNF

BOGR

SCP

ANGE

BOGR

SCSC

SEV

BOER

BOGR

SFA

SCSC

UHC

ANGE

SCSC

Family

Bacteria-Archaea

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER

BOGR

GMT

BOER

BOGR

GNF

SCSC

HAR

ANGE

BOGR

BUDA

SCSC

HPG

BOGR

BUDA

KAE

ANGE

BUDA

SCSC

KNZ

ANGE

BOGR

BUDA

SCSC

LAR

ANGE

BOGR

SCSC

LBJ

ANGE

BUDA

SCSC

NWP

ANGE

SCSC

ONF

ANGE

SCSC

RNF

BOGR

SEV

BOER

BOGR

SFA

SCSC

UHC

ANGE

SCSC

Fungi

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER

BOGR

GMT

BOER

BOGR

GNF

BOGR

SCSC

HAR

ANGE

BOGR

BUDA

SCSC

HPG

BOGR

BUDA

KAE

ANGE

BUDA

SCSC

KNZ

ANGE

BOGR

BUDA

SCSC

LAR

ANGE

BOGR

SCSC

LBJ

ANGE

BUDA

SCSC

NWP

ANGE

SCSC

ONF

ANGE

SCSC

RNF

BOGR

SCP

ANGE

BOGR

SCSC

SEV

BOER

BOGR

SFA

SCSC

UHC

ANGE

SCSC

KSU paired amplicon Taxonomy

Taxa plots of clean 16S and ITS OTU tables (EDGE samples excluded)

Kelli Feeser

2023-09-20

Set-up project naming from reproducible code

Set-up

1 Import clean OTU tables

Set-up metadata strata

Available metadata

Set-up taxonomy for plotting

1.1 Global: Calculate mean relative abundance by tax rank and export csv of relative abundance

1.1.1 merge samples within dataset

1.1.2 tax glom and export

Samples: Calculate relative abundance by tax rank and export csv of relative abundance

1.1.3 generating code:

1.1.4 create vectors of top 10 taxa names

1.1.4.1 Bac

1.1.4.2 Fun

1.2 Group low abundance (!%inc% top 10 taxa)

1.2.1 Bac

1.2.2 Fun

1.3

1.4 restore dat files

Full Dataset

Grass replicates, grouped by Site

Phylum

Bacteria-Archaea

Fungi

Class

Bacteria-Archaea

Fungi

Order

Bacteria-Archaea

Fungi

Family

Bacteria-Archaea

Fungi

Site replicates, grouped by Grass

Phylum

Bacteria-Archaea

Fungi

Class

Bacteria-Archaea

Fungi

Order

Bacteria-Archaea

Fungi

Family

Bacteria-Archaea

Fungi

Across Different Sites by Grass Hosts

Phylum

Bacteria-Archaea

BLM

BOGR

SCSC

BNP

BOER

BOGR

CAD

ANGE

BOGR

BUDA

SCSC

CNF

BOER

BOGR

SCSC

CPR

BOER

DMT

BOER

BOGR

SCSC

FCP

BOGR

BUDA

SCSC

FMT

BOER